The low nutritive value of maize endosperm protein is genetically corrected in quality protein maize (QPM), which contains the opaque 2 gene along with numerous modifiers for kernel hardness. We report here a two generation marker-based backcross breeding program for incorporation of the opaque 2 gene along with phenotypic selection for kernel modification in the background of an early maturing normal maize inbred line, V25. Using the flanking marker distances from opaque 2 gene in the cross V 25 xCML 176, optimum population size for the BC(2) generation was computed in such a way that at least one double recombinant could be obtained. Whole genome background selection in the BC(2) generation identified three plants with 93 to 96% recurrent parent genome content. The three BC(2)F(2) families derived from marker identified BC(2) individuals were subjected to foreground selection and phenotypic selection for kernel modification. The tryptophan concentration in endosperm protein was significantly enhanced in all the three classes of kernel modification viz., less than 25%, 25--50% and more than 50% opaqueness. BC(2)F(3) lines developed from the hard endosperm kernels were evaluated for desirable agronomic and biochemical traits in replicated trials and the best line was chosen to represent the QPM version of V25, with tryptophan concentration of 0.85% in protein. The integrated breeding strategy reported here can be applied to reduce genetic drag as well as the time involved in a conventional line conversion program, and would prove valuable in rapid development of specialty corn germ plasm.

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Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

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